The TNF-Like Protein 1A–Death Receptor 3 Pathway Promotes Macrophage Foam Cell Formation In Vitro
Bibliographic record
Abstract
TNF-like protein 1A (TL1A), a TNF superfamily cytokine that binds to death receptor 3 (DR3), is highly expressed in macrophage foam cell-rich regions of atherosclerotic plaques, although its role in foam cell formation has yet to be elucidated. We investigated whether TL1A can directly stimulate macrophage foam cell formation in both THP-1 and primary human monocyte-derived macrophages with the underlying mechanisms involved. We demonstrated that TL1A promotes foam cell formation in human macrophages in vitro by increasing both acetylated and oxidized low-density lipoprotein uptake, by enhancing intracellular total and esterified cholesterol levels and reducing cholesterol efflux. This imbalance in cholesterol homeostasis is orchestrated by TL1A-mediated changes in the mRNA and protein expression of several genes implicated in the uptake and efflux of cholesterol, such as scavenger receptor A and ATP-binding cassette transporter A1. Furthermore, through the use of virally delivered DR3 short-hairpin RNA and bone marrow-derived macrophages from DR3 knockout mice, we demonstrate that DR3 can regulate foam cell formation and contributes significantly to the action of TL1A in this process in vitro. We show, for the first time, a novel proatherogenic role for both TL1A and DR3 that implicates this pathway as a target for the therapeutic intervention of atherosclerosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".